Category: Analytics

Thinking about the future is not easy.

As humans we fall prey to biases, and two in particular are important.

The first is hindsight bias where, looking back, we think that things that have happened were far more inevitable than they actually were.

For example a Trump victory seems like it was pre-ordained now – Hillary never stood a chance against the Twitter machine.

At the time, however, not many around the world seriously thought Trump would win.

The second is foresight bias – we believe some things are more likely to happen than others and so bet on them more heavily.

We need tools and methods to guard against these biases and reason about the future more effectively – and the military and intelligence establishments are a good source of information on these.

For example, this guide sets out a detailed approach to counterfactual reasoning, one of the tools every analyst should be able to use.

When we think about the future we often do one of two things.

1. We look at trends

We see trends and infer outcomes that result from those trends – a technique called forecasting.

For example, we might see a trend towards decentralised currencies with bitcoin or a trend towards widescale adoption of solar photovoltaic and distributed generation.

We forecast an outcome based on these trends – the end of traditional banking or energy firms.

2. We create possible futures

We do futuring when we look at drivers and come up with possible scenarios that might result.

For example, the widespread use of mobile phones will make desktop or offline services less relevant for things like getting media, checking mail and reading the news.

Counterfactual reasoning

Counterfactual means counter to the facts, and we reason that way by asking questions like “What if” or “If we”.

We can look at a problem in terms of antecedents and precedents – or before and after a fact.

Approaching a problem in this way has two benefits – it helps us explore cause and effects and it lets us be more creative.

For example, take a statement like the fall in the price of solar panels means that we will have widespread adoption in residential neighbourhoods.

That seems like a perfectly reasonable statement – but what happens if we break it down?

Should we start a solar panels sales business right now?

The before bit is a fall in the price of solar panels – which we see happening right now.

Cheap solar panels clearly lead to cheaper costs for the equipment.

But, does that alone justify the conclusion about what comes after – widespread adoption in residential neighbourhoods?

It does not – because we haven’t looked at the components in detail.

First, we need to examine why prices are low. Is it because the technology is getting better and cheaper, or is it because massive capacity increases in China are resulting in panels being dumped on the world market?

Then we need to think about the in-between – what may happen if what we predict takes place.

Low prices for panels don’t get around other problems – such as the connection constraints in neighbourhood, the other costs of installation such as scaffolding, and the possibility that high demand for installations coupled with low numbers of qualified tradespeople after BREXIT may result in bumping up the costs overall.

Then there is the after – new homes are very likely to have panels fitted – they can be designed in.

But will there be a rush by homeowners to retrofit panels or will they be put off by the up front cost and possible impact on sale prices?

If existing homes are slow to change, the overall rate of change will be slow because existing housing stock stays in place for decades so for everything to be replaced with new energy-efficient housing could take a century.

Summary

We can jump very quickly from what we see now to what we think will happen in the future.

The purpose of using analytic methods in a structured way is to help slow us down and examine the situation in more detail, coming to a more considered view on what may happen.

The conclusions we come to as a result may help us make better decisions.

We often think of technology as a good thing – surely having the latest version of something is obviously the best way and people who do that win?

Perhaps not.

Charlie Munger said – The great lesson in microeconomics is to discriminate between when technology is going to help you and when it’s going to kill you.

Most companies would benefit from new production technology that is more efficient and so uses less energy.

The deciding factor, however, is what that technology does for the business

Does it help it create more products, for example.

In a commodity business, being able to push more product out means that the market has more supply and so prices go down.

The cost and energy savings made by the more efficient technology is wiped out by the reduction in prices to customers.

All of the benefit goes the customer, with little staying with the manufacturer.

The Japanese are well-known for having slow upgrade cycles, using older equipment for much longer.

This is because changing things adds complexity and could reduce the amount of time the factory actually operates.

In addition, changes often introduce new problems, and Japanese companies value stability and continuity.

They invest in systems that help them reduce defects, by continually monitoring a number of parameters and warning them when things are going wrong.

This helps them maintain quality.

Having good monitoring systems lets workers manage more systems and machines each – while good working practices, maintenance regimes and stable technology let operations carry on without crisis or constant intervention.

All too often, we look for a silver bullet – a new technology solution that will solve all our problems.

We should start, however, by making sure that we are using what we already have well – and good monitoring systems are our eyes and ears into the operations.

It’s simple really – we need to do the basic things a little better, every day.

And that starts with looking and improving what is in place before buying something new.

The UK economy is dominated by the service sector, which makes up more than 80% of GDP.

Many industrialised countries are in a similar position, moving away from raw material extraction and manufacturing to an economy based on service and, increasingly, knowledge based activities.

How should we think about service businesses?

We often start by thinking of a service as something people do for other people but this doesn’t capture the full picture.

In 1978 Dan R.E. Thomas, writing in the Harvard Business Review, suggested that we need to ask two questions to understand the model used in a given service business:

1. How is the service rendered?
2. What equipment or people render the service?

Matching services with business models

Although the article is old, it can be adapted into a framework to help match services with business models.

On one axis, we can think of people and their skills, ranging from relatively unskilled to professionals with extensive qualifications.

On the other, we set out how they use equipment and whether it needs to be operated, monitored or can be automated.

Services that require a human operator range from mowing a lawn, which can be done by someone relatively unskilled with a mower, to heart surgery, which requires a team of professionals with specialised equipment and facilities.

Monitored services can range from overseeing equipment, such as a car wash to more complex plant operations and consulting services.

In these situations the people don’t need to get physically involved but use systems to keep track of operations and change settings as needed.

Automated services range from vending machines at one extreme that have a fairly straightforward task of dispensing products to expert systems such as a health website that allows us to diagnose ourselves and decide whether we need to go to a hospital or not.

Why knowing the kind of service model you have is important

The kind of service model we operate decides how we scale the business.

If a business depends on one person’s time to succeed, then scale can only happen by adding more similar people.

Think lawyers, accountants and management consultants.

There is a reason why most professional practices are small.

They can only grow by putting in more capital and pushing up their fixed costs base which, if revenue fails to grow as expected, means they eventually slide into failure.

There aren’t that many ways to get around this. A common solution is to find patrons – small or big.

Scaling equipment, on the other hand, may be an easier option.

As more of the service is automated, the same number of professionals can deliver a better service to customers.

Unless it spills over into self-service.

There is a crucial difference between service automation that makes things better and cheaper for users and service automation that makes things better and cheaper for providers.

Getting users to do more of the work can easily fall into the latter category.

The right blend of service and equipment

A good service business, it would seem, has a core of people with appropriate skills and scales by adding technology and automation that improves service quality to customers before adding more people.

As with most things, that’s easy to say, but not simple to do.

The natural world is teeming with creatures perfectly adapted to their environment – that have ways of walking, swimming and flying, live alone or in social groups and participate in an ecosystem with their own unique niche and capabilities?

Where do we begin trying to understand how they do it?

We start by breaking things down into parts that we can understand.

Like blind people touching parts of an elephant, we find pieces – a snake-like tail, a fan-like ear, a tree-like leg.

If we bolted a snake, a log and a fan together with the other bits that we identified, would we get an elephant?

The answer is clearly no – but we persist in trying to build complicated things from simpler pieces.

Take most systems, for example.

An organisation is a system made up of people in roles.

There are some at the top who see themselves as the brains and controllers of the outfit and many people who do work.

Organisations are often designed – made up of structures and hierarchies and reporting lines – held together and moved in a particular direction by incentives, punishments and guidance.

Does organisational behaviour come from the particular arrangement and positioning of people?

Or does it emerge from somewhere else?

The study of emergence looks at how complex behaviour arises from the interaction between simpler elements.

There is a difference between complex and complicated.

Complicated may be something like a steam train – with lots of moving parts. When the parts move in the way they should, we get something complicated like a moving train.

An example of a complex thing is a flock of birds flying in the sky together. Each bird maintains its distance from another – and the whole flock can swoop and move like a single living thing – but there is no one bird that plans or controls the action.

The complex thing that we can relate to easily is the Internet.

We are all connected by a vast decentralised network that has only a few simple rules about pages and links – but is so much more than that now.

Emergence is sometimes seen as the border between order and chaos.

In an ordered world, everything has its place – we put a rock on top of another rock and eventually we can get a building.

A chaotic world is dynamic – as elements combine randomly with feedback to create new conditions that – and range from the weather to swirls in a coffee mug.

As we move from order to chaos – we pass through emergence – and that is where life and the behaviour we see in the natural world seems to be.

But how can we use this in daily life or business?

With knowledge work in particular, a strict rules based approach is unlikely to create anything particularly interesting or innovative.

Instead, its the interaction between people with capabilities working together that creates output from the organisation that is “greater than the sum of its parts”.

Managers should try and do just a few things.

1. Find good people.
2. Remove as many barriers as possible that stop them working together.
3. Set a few working practices
4. Get out of their way.

Then, wait to see what emerges.

We often put a lot of effort into creating a good looking dashboard or reporting system – but then what happens?

All too often it simply becomes background noise.

We get it every day and ignore or simply tune out – going blind to it while we get on with whatever is latest and loudest.

We tend to fall into patterns and things like data and information displays can simply become pretty pictures rather than being used for reflection and action.

Unless you work in a lean organisation, that is.

The Japanese have a word for a big room – Obeya – that they use to call a dedicated space that is where people come together, can see what is going on and collaborate.

A way to think about this is like a command centre, a war room or the bridge of a ship.

Japanese companies like Toyota use this every day – or even several times a day.

It starts with a simple idea – we have to hit certain targets every day.

We get together during the day and go through the numbers and see if we are on track or not.

If not, we can make changes and correct our course. Being in the same space helps with having that conversation.

The space doesn’t have to be physical – it can be a digital space where we can get together, share and modify information.

But the important bit is that we need to engage with information we get.

At Toyota they make updating information a manual activity – writing numbers by hand, drawing charts and updating status indicators.

It’s the process of interacting with the data and information we have in front of us – of trying to touch and feel it – that transforms it from being a pretty picture on the wall to the source of our next action.

We are more engaged when we understand what is in front of us – and that makes for better conversations and more useful collaboration between colleagues.

It’s interesting that as we have more and more powerful ways to dissect and distill the data around us we humans become the bottlenecks in being able to use the information and insights more effectively.

And that’s no bad thing – constraints are good for innovation.

If anything – we need to slow down even more.

We need to look beyond the dashboard as a result and focus on the end result – what are we trying to achieve each day?

We should get our fancy algorithms and computers to do the number crunching they are good at and give us the figures we need – that’s not work people should do.

Our job to use our time to get a feel for the numbers – getting tactile with them.

That frees us up to use our creative ability to come up with solutions – and we need all the creative time we can get.

Because there are a lot of interesting problems out there to solve.

Artificial Intelligence (AI) has been just around the corner for a long time – but looks like we have now arrived.

Computers can beat us at Chess and Go and respond to voice commands.

Navigation systems are so good many of us now have never really learned to use a map.

There are so many ways of looking at and classifying the field of AI and machine learning that it’s almost impossible to get a sense of the field.

But we can start by looking at some broad domains – what do humans do a lot of the time?

We sense things – taking in vast quantities of visual, auditory and tactile information and responding to our environment.

We can detect the edges of things, work out which way is up or down and work out what is near us and if we are going to bang into them.

A particularly human thing to do is reason. Our brains are essentially prediction machines – we can think about what has happened and use reasoning to work out what we should do next.

But we don’t exist in isolation – as social creatures we interact with others – listening, speaking and responding.

We make plans – choose between alternatives or options – that range from what to eat to how to get somewhere.

We are also teleological – the conscious part of our brain helps us do things with purpose.

Our brains have evolved to be the way they are – but how would we go about creating an artificial one?

We could start by writing down all the rules we follow.

For example, doctors get to a diagnosis by considering and eliminating possibilities based on the symptoms they see and the measurements they take.

Rule based or expert systems take all this knowledge and use it to create if-then rules – if the temperature is above X, check Y next.

These systems are now pretty effective – and help us select the best flight, the cheapest online store for an item and schedule calendar entries from text in emails.

If there is too much data and variation to come up with rules, then we might use probabilistic approaches.

For example, we can run weather simulations that are probably accurate over hours or days but less so over weeks and months.

We can look at the distribution of a time series and use that to predict the range of probable future values – which then lets us pick out values and events that fall outside expected levels.

The rule based and probabilistic approaches are pretty easy to build and many systems in use now will be based on them.

A more complex approach is pattern matching, where a learning algorithm adjusts itself and learns from the data that goes into it.

For example, every time we type a search term into Google, we are training its AI engines. If we type in the word “eagle” and then click on a picture of an eagle, Google can learn what eagles look like and eventually predict that a picture contains an eagle.

With pattern matching, the more information we have the better our algorithm gets – and so it’s a winner take all situation where the systems we interact with most will learn the most and pull away from the rest.

But where can we use this technology now?

Three areas that are of interest in the energy sector are forecasting, scheduling and trading.

The energy system is all about balancing supply and demand, whether at the grid level or the domestic level.

If we know when the wind is going to blow, then we can make a call on the number of fossil fuelled power stations we need.

If we can see when demand or prices are high, we can schedule when we do work to avoid costs or take advantage of high prices.

We could even trade between ourselves – selling or buying electricity from the grid or a peer-to-peer network for a profit.

An interesting thing that happens with AI is that as it gets cleverer we tend to dismiss the things it does as simply something a machine can do.

As a result it is quietly augmenting how we do things without us really noticing. For example, how many of us now choose a different route based on Google’s recommendations first thing in the morning?

Many AI applications will be almost unnoticed, simply transforming the essential building blocks of our economic system.

Eventually, one hopes, AI will free humans up to do more creative fulfilling work and leave the mundane to the machines.